D. A. van der Windt

Journal of Clinical Epidemiology · 2000–2013

A researcher in the Science corpus: 4 papers; trusts 1 researcher and is trusted by 3 researchers here, as inferred from the citation graph.

Whom D. A. van der Windt trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. for the Statistics Subcommittee of the NCI—EORTC Worki Diagnostics
  2. Patrick Royston
  3. Karel G. M. Moons
  4. J. Hayden
  5. Douglas G. Altman
  6. Ewout W. Steyerberg
  7. Pablo Perel

Who trusts D. A. van der Windt overall

  1. Karel G. M. Moons
  2. Gary S. Collins
  3. M. Simmonds
  4. A. Llewellyn
  5. R. Riley
  6. E. Coomes

Papers from D. A. van der Windt's perspective

Ordered by how strongly the researchers D. A. van der Windt trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. (American Journal of Epidemiology)
  2. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers (Statistics in Medicine)
  3. Updating methods improved the performance of a clinical prediction model in new patients. (Journal of Clinical Epidemiology)
  4. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients (British medical journal)
  5. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation.
  6. Prognosis and prognostic research: application and impact of prognostic models in clinical practice (British medical journal)
  7. Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions
  8. Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research (PLoS Medicine)
  9. The Nottingham prognostic index in primary breast cancer (Breast Cancer Research and Treatment)
  10. Multivariable Model-Building: A Pragmatic Approach to Regression Analysis based on Fractional Polynomials for Modelling Continuous Variables

Their papers